A Bayesian approach for predicting the popularity of tweets
Tauhid Zaman, Emily B. Fox, Eric T. Bradlow
The Annals of Applied Statistics · 2014 · Volume 8 · Issue 3
Abstract
We predict the popularity of short messages called tweets created in the micro-blogging site known as Twitter. We measure the popularity of a tweet by the time-series path of its retweets, which is when people forward the tweet to others. We develop a probabilistic model for the evolution of the retweets using a Bayesian approach, and form predictions using only observations on the retweet times and the local network or “graph” structure of the retweeters. We obtain good step ahead forecasts and predictions of the final total number of retweets even when only a small fraction (i.e., less than one tenth) of the retweet path is observed. This translates to good predictions within a few minutes of a tweet being posted, and has potential implications for understanding the spread of broader ideas, memes or trends in social networks.
Keywords
Bayesian inference, forecasting, social networks, time series, Twitter
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